Software Bug Prediction Prototype Using Bayesian Network Classifier: A Comprehensive Model
| Autor: | Ravi Bhushan Mishra, Sushant Kumar Pandey, Anil Kumar Triphathi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
business.industry Software development Bayesian network 020207 software engineering 02 engineering and technology Construct (python library) Machine learning computer.software_genre Naive Bayes classifier Software Software bug 0202 electrical engineering electronic engineering information engineering Code (cryptography) General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence business computer General Environmental Science |
| Zdroj: | Procedia Computer Science. 132:1412-1421 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2018.05.071 |
| Popis: | Software bug prediction becomes the vital activity during software development and maintenance. Fault prediction model able to engaged to identify flawed software code by utilizing machine learning techniques. Naive Bayes classifier has often used times for this kind problems, because of its high predictive performance and comprehensiveness toward most of the predictive issues. Bayesian network(BN) able to construct the simple network of a complex problem using the fewer number of nodes and unexplored arcs. The dataset is an essential phase in bugs prediction, NASA/Eclipse free-ware are freely available for better results. ROC/AUC is a performance measure for classification of fault-prone or non-fault prone, H-measure is also useful while prediction technique, we will explore every parameter and valuable expects for experiment perspective. |
| Databáze: | OpenAIRE |
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